The most recent update of this html document occurred: Mon Nov 28 10:40:03 2016
> library(knitr)
>
> library(ggplot2)
> library(reshape)
> library(DESeq2)
> library(genefilter)
> library(CHBUtils)
> library(gtools)
> library(gridExtra)
> library(devtools)
> library(dplyr)
> library(isomiRs)
> library(pheatmap)
>
>
> root_path = "~/orch/scratch/ec_brain_miRNA/mirna_rat_brain/work/upload"
> root_file = file.path(root_path, "srna_out_files")
> dir.create(root_file, showWarnings = FALSE)
>
> metadata_fn = list.files(file.path(root_path), pattern = "summary.csv$", recursive = T,
+ full.names = T)
> metadata = read.csv(metadata_fn, row.names = "sample_id")
> condition = names(metadata)[1]
> design = metadata[, "group", drop = FALSE]
> formula = ~condition # modify this to get your own formula, it should be a column in your metadata
> isde = FALSE # turn this true to make DE ananlysisIn this section we will see descriptive figures about quality of the data, reads with adapter, reads mapped to miRNAs, reads mapped to other small RNAs.
After adapter removal, we can plot the size distribution of the small RNAs.
> files = list.files(file.path(root_path), pattern = "trimming_stats", recursive = T)
> isadapter = length(files) > 0> names(files) = sapply(files, function(x) {
+ gsub("-ready.trimming_stats", "", basename(x))
+ })
>
>
> tab = data.frame()
> for (sample in rownames(metadata)) {
+ d = read.table(file.path(root_path, files[sample]), sep = " ")
+ tab = rbind(tab, d %>% mutate(sample = sample, group = metadata[sample,
+ condition]))
+ }
>
>
> reads_adapter = tab %>% group_by(sample, group) %>% summarise(total = sum(V2))
> ggplot(reads_adapter, aes(x = sample, y = total, fill = group)) + geom_bar(stat = "identity",
+ position = "dodge") + ggtitle("total number of reads with adapter") + ylab("# reads") +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))> ggplot(tab, aes(x = V1, y = V2, group = sample)) + geom_bar(stat = "identity",
+ position = "dodge") + facet_wrap(~group, ncol = 2) + ggtitle("size distribution") +
+ ylab("# reads") + xlab("size") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))> files = list.files(file.path(root_path), pattern = "mirbase-ready", recursive = T,
+ full.names = T)
> ismirbase = length(files) > 0
> mirdeep2_files = list.files(file.path(root_path), pattern = "novel-ready", recursive = T,
+ full.names = T)
> ismirdeep2 = length(mirdeep2_files) > 0> names(files) = sapply(files, function(x) {
+ gsub("-mirbase-ready.counts", "", basename(x))
+ })
>
> obj <- IsomirDataSeqFromFiles(files = files[rownames(design)], design = design,
+ header = T, skip = 0)> ggplot(data.frame(sample = colnames(counts(obj)), total = colSums(counts(obj)))) +
+ geom_bar(aes(x = sample, y = total), stat = "identity") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))> mirna_step <- as.data.frame(colSums(counts(obj)))> ggplot(melt(counts(obj))) + geom_boxplot(aes(x = X2, y = value)) + scale_y_log10() +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))> cs <- as.data.frame(apply(counts(obj), 2, function(x) {
+ cumsum(sort(x, decreasing = T))
+ }))
> cs$pos <- 1:nrow(cs)
>
> ggplot((melt(cs, id.vars = "pos"))) + geom_line(aes(x = pos, y = value, color = variable)) +
+ scale_y_log10()> counts = counts(obj)
> dds = DESeqDataSetFromMatrix(counts[rowSums(counts > 0) > 3, ], colData = design,
+ design = ~1)
> vst = rlog(dds)
>
> pheatmap(assay(vst), annotation_col = design, show_rownames = F, clustering_distance_cols = "correlation",
+ clustering_method = "ward.D")> mds(assay(vst), condition = design[, condition])Number of miRNAs with > 3 counts.
> kable(as.data.frame(colSums(counts > 10)))| colSums(counts > 10) | |
|---|---|
| I14F | 449 |
| I17F | 399 |
| I15M | 446 |
| I19M | 462 |
| G16F | 471 |
| I17M | 414 |
| G12F | 460 |
| G18M | 427 |
| G13F | 445 |
| G15F | 457 |
| G21M | 466 |
| G17F | 457 |
| I12F | 440 |
| G19M | 423 |
| I10F | 459 |
| I16F | 364 |
| I20M | 450 |
| G16M | 450 |
| I22M | 431 |
| G20M | 477 |
> files = mirdeep2_files
>
> names(files) = sapply(files, function(x) {
+ gsub("-novel-ready.counts", "", basename(x))
+ })
>
> obj_mirdeep <- IsomirDataSeqFromFiles(files = files[rownames(design)], design = design,
+ header = T)> ggplot(data.frame(sample = colnames(counts(obj_mirdeep)), total = colSums(counts(obj_mirdeep)))) +
+ geom_bar(aes(x = sample, y = total), stat = "identity") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))> mirna_step <- as.data.frame(colSums(counts(obj)))> ggplot(melt(counts(obj_mirdeep))) + geom_boxplot(aes(x = X2, y = value)) + scale_y_log10() +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))> cs <- as.data.frame(apply(counts(obj_mirdeep), 2, function(x) {
+ cumsum(sort(x, decreasing = T))
+ }))
> cs$pos <- 1:nrow(cs)
>
> ggplot((melt(cs, id.vars = "pos"))) + geom_line(aes(x = pos, y = value, color = variable)) +
+ scale_y_log10()> counts = counts(obj_mirdeep)
> dds = DESeqDataSetFromMatrix(counts[rowSums(counts > 0) > 3, ], colData = design,
+ design = ~1)
> vst = rlog(dds)
>
> pheatmap(assay(vst), annotation_col = design, show_rownames = F, clustering_distance_cols = "correlation",
+ clustering_method = "ward.D")> mds(assay(vst), condition = design[, condition])Number of miRNAs with > 3 counts.
> kable(as.data.frame(colSums(counts > 10)))| colSums(counts > 10) | |
|---|---|
| I14F | 309 |
| I17F | 273 |
| I15M | 302 |
| I19M | 366 |
| G16F | 246 |
| I17M | 287 |
| G12F | 317 |
| G18M | 258 |
| G13F | 298 |
| G15F | 342 |
| G21M | 340 |
| G17F | 241 |
| I12F | 291 |
| G19M | 253 |
| I10F | 313 |
| I16F | 239 |
| I20M | 293 |
| G16M | 278 |
| I22M | 294 |
| G20M | 339 |
The data was analyzed with seqcluster
This tools used all reads, uniquely mapped and multi-mapped reads. The first step is to cluster sequences in all locations they overlap. The second step is to create meta-clusters: is the unit that merge all clusters that share the same sequences. This way the output are meta-clusters, common sequences that could come from different region of the genome.
In this table 1 means % of the genome with at least 1 read, and 0 means % of the genome without reads.
> fn_json = list.files(file.path(root_path), pattern = "seqcluster.json", recursive = T,
+ full.names = T)
> seq_dir = dirname(fn_json)
>
> isseqcluster = length(fn_json) > 0
> # cov_stats <- read.table(file.path(root_path, '..', 'align',
> # 'seqs_rmlw.bam_cov.tsv'),sep='\t',check.names = F)
>
> # kable(cov_stats[cov_stats$V1=='genome',] %>%
> # dplyr::select(coverage=V2,ratio_genome=V5), row.names = FALSE)The normal value for human data with strong small RNA signal is: 0.0002. This will change for smaller genomes.
Number of reads in the data after each step:
> reads_stats <- read.table(file.path(seq_dir, "read_stats.tsv"), sep = "\t",
+ check.names = F)
> ggplot(reads_stats, aes(x = V2, y = V1, fill = V3)) + geom_bar(stat = "identity",
+ position = "dodge") + labs(list(x = "samples", y = "reads")) + scale_fill_brewer("steps",
+ palette = "Set1") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5,
+ hjust = 1))> clus <- read.table(file.path(seq_dir, "counts.tsv"), header = T, sep = "\t",
+ row.names = 1, check.names = FALSE)
> ann <- clus[, 2]
> toomany <- clus[, 1]
> clus_ma <- clus[, 3:ncol(clus)]
> clus_ma = clus_ma[, row.names(design)]Check complex meta-clusters: This kind of events happen when there are small RNA over the whole genome, and all repetitive small rnas map to thousands of places and sharing many sequences in many positions. If any meta-cluster is > 40% of the total data, maybe it is worth to add some filters like: minimum number of counts -e or --min--shared in seqcluster prepare
> library(edgeR)
> clus_ma_norm = cpm(DGEList(clus_ma), normalized.lib.sizes = TRUE)
> head(clus_ma_norm[toomany > 0, ]) I14F I17F I15M I19M G16F I17M G12F G18M G13F G15F G21M G17F I12F G19M
I10F I16F I20M G16M I22M G20M
Number of miRNAs with > 10 counts.
> kable(as.data.frame(colSums(clus_ma > 10)))| colSums(clus_ma > 10) | |
|---|---|
| I14F | 713 |
| I17F | 691 |
| I15M | 707 |
| I19M | 720 |
| G16F | 703 |
| I17M | 702 |
| G12F | 715 |
| G18M | 702 |
| G13F | 707 |
| G15F | 714 |
| G21M | 708 |
| G17F | 712 |
| I12F | 702 |
| G19M | 697 |
| I10F | 698 |
| I16F | 687 |
| I20M | 711 |
| G16M | 709 |
| I22M | 703 |
| G20M | 716 |
> rRNA <- colSums(clus_ma[grepl("rRNA", ann) & grepl("miRNA", ann) == F, ])
> miRNA <- colSums(clus_ma[grepl("miRNA", ann), ])
> tRNA <- colSums(clus_ma[grepl("tRNA", ann) & grepl("rRNA", ann) == F & grepl("ncRNA",
+ ann) == F & grepl("miRNA", ann) == F, ])
> rmsk <- colSums(clus_ma[grepl("ncRNA", ann) & grepl("rRNA", ann) == F & grepl("miRNA",
+ ann) == F, ])
> total <- colSums(clus_ma)
>
> dd <- data.frame(samples = names(rRNA), rRNA = rRNA, miRNA = miRNA, tRNA = tRNA,
+ ncRNA = rmsk, total = total)
> ggplot(melt(dd)) + geom_bar(aes(x = samples, y = value, fill = variable), stat = "identity",
+ position = "dodge") + scale_fill_brewer(palette = "Set1") + theme(axis.text.x = element_text(angle = 90,
+ vjust = 0.5, hjust = 1))> dd_norm = dd
> dd_norm[, 2:5] = sweep(dd[, 2:5], 1, dd[, 6], "/")
> ggplot(melt(dd_norm[, 1:5])) + geom_bar(aes(x = samples, y = value, fill = variable),
+ stat = "identity", position = "dodge") + scale_fill_brewer(palette = "Set1") +
+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
+ labs(list(title = "relative proportion of small RNAs", y = "% reads"))> # size_clus <- read.table(file.path(root_path, '..', 'seqcluster',
> # 'cluster', 'size_counts.tsv'),sep='\t',check.names = F)> mds(log2(clus_ma_norm + 1), condition = design[, condition])DESeq2 is used for this analysis.
> library(DESeq2)
> # library(DEGreport)
> library(vsn)
> formula = ~group
> isde = TRUE> #' save file
> save_file <- function(dat, fn, basedir = ".") {
+ tab <- cbind(id = data.frame(id = row.names(dat)), as.data.frame(dat))
+ write.table(tab, file.path(basedir, fn), quote = F, sep = "\t", row.names = F)
+ }
>
> filter_handle <- function(res) {
+ res_nona <- res[!is.na(res$padj), ]
+ keep <- res_nona$padj < 0.1
+ res_nona[keep, ]
+ }
>
> handle_deseq2 = function(dds, summarydata, column, prefix, all_combs = NULL) {
+ if (is.null(all_combs))
+ all_combs = combn(levels(summarydata[, column]), 2, simplify = FALSE)
+ all_results = list()
+ contrast_strings = list()
+ rlog = rlog(dds)
+ for (comb in all_combs) {
+ contrast_string = paste(comb, collapse = "_vs_")
+ cat("\n\n## Comparison: ", contrast_string, "\n")
+ contrast = c(column, comb)
+ res = results(dds, contrast = contrast)
+ res = res[order(res$padj), ]
+ all_results = c(all_results, res)
+ contrast_strings = c(contrast_strings, contrast_string)
+ samples = row.names(summarydata)[summarydata[, column] %in% comb]
+ print_out(dds, rlog, res, paste0(prefix, "_", contrast_string), samples = samples)
+ }
+ names(all_results) = contrast_strings
+ return(all_results)
+ }
>
> do_de = function(raw, summarydata, condition, minc = 3) {
+ dss = DESeqDataSetFromMatrix(countData = raw[rowMeans(raw) > minc, ], colData = summarydata,
+ design = ~condition)
+ dss = DESeq(dss)
+ dss
+ }
>
> do_norm = function(dss, path, prefix) {
+ rlog_ma = assay(rlog(dss))
+ count_ma = counts(dss, normalized = TRUE)
+ raw = counts(dss, normalized = FALSE)
+
+ fn_log = paste0(prefix, "_log_matrix.txt")
+ save_file(rlog_ma, fn_log, path)
+
+ fn_count = paste0(prefix, "_norm_matrix.txt")
+ save_file(count_ma, fn_count, path)
+
+ fn_raw = paste0(prefix, "_raw_matrix.txt")
+ save_file(raw, fn_raw, path)
+ }
>
> print_out = function(dss, rlog = NULL, res = NULL, prefix = "standard_", samples = NULL) {
+ plotDispEsts(dss)
+ if (is.null(res))
+ res = results(dss)
+ if (is.null(rlog))
+ rlog = rlog(dss)
+
+ rlogmat = assay(rlog)
+ if (!is.null(samples))
+ rlogmat = rlogmat[, samples]
+
+ design = as.data.frame(colData(dss)[samples, names(colData(dss)) != "sizeFactor",
+ drop = FALSE])
+
+ out_df = as.data.frame(res)
+ out_df = out_df[!is.na(out_df$padj), ]
+ out_df = out_df[order(out_df$padj), ]
+ # do_norm(dss, root_file, prefix)
+
+ cat("\n", paste(capture.output(summary(res))[1:8], collapse = "<br>"), "\n")
+ cat("\n\n### MA plot plot\n\n")
+ DESeq2::plotMA(res)
+
+ cat("\n\n### Top DE miRNAs\n\n")
+ print(kable(head(out_df, 20)))
+ fn = paste(prefix, ".tsv", sep = "")
+ save_file(out_df, fn, root_file)
+
+ sign = row.names(out_df)[out_df$padj < 0.05 & !is.na(out_df$padj) & abs(out_df$log2FoldChange) >
+ 0.5]
+
+ cat("\n\n### Heatmap most significant(", length(sign), "), padj<0.05 and log2FC > 0.5\n")
+ if (length(sign) < 10) {
+ cat("Too few genes to plot.")
+ } else {
+ pheatmap(rlogmat[sign, ], show_rownames = F, annotation_col = design,
+ clustering_distance_cols = "correlation", clustering_method = "ward.D2")
+ print(mds(rlogmat[sign, ], condition = design[, condition]))
+ }
+ len = out_df %>% filter(padj < 0.05) %>% count() %>% unlist()
+ if (len > 10) {
+ len = 10
+ }
+ cat("\n\n### Top genes \n\n")
+ DEGreport::degPlot(dss, out_df, n = len, xs = "group", group = "group")
+ }> design$group = factor(design$group, levels = c("IsoF", "IsoM", "GroupedF", "GroupedM"))
> counts = counts(obj)
> dss = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) > 3, ],
+ colData = design, design = formula)
> dss = DESeq(dss)
>
> all_results = handle_deseq2(dss, design, condition, "mirna_")
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 1, 0.19%
outliers [1] : 16, 3%
low counts [2] : 0, 0%
(mean count < 1)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-miR-146a-5p | 4.906063e+02 | -1.3433217 | 0.3348226 | -4.012040 | 0.0000602 | 0.0311817 |
| rno-miR-204-5p | 1.565829e+04 | -1.3387941 | 0.4231114 | -3.164165 | 0.0015553 | 0.2685463 |
| rno-miR-873-5p | 1.224483e+03 | 0.8683013 | 0.2730759 | 3.179707 | 0.0014742 | 0.2685463 |
| rno-miR-148b-3p | 1.891634e+04 | -0.7836821 | 0.2704831 | -2.897342 | 0.0037634 | 0.4873596 |
| rno-miR-664-2-5p | 2.114094e+03 | 0.7487052 | 0.2743208 | 2.729305 | 0.0063468 | 0.6575280 |
| rno-let-7a-1-3p | 4.741120e+02 | -0.6907294 | 0.2691926 | -2.565930 | 0.0102900 | 0.6662747 |
| rno-let-7c-2-3p | 4.741120e+02 | -0.6907294 | 0.2691926 | -2.565930 | 0.0102900 | 0.6662747 |
| rno-miR-34a-5p | 6.392179e+02 | -0.7851366 | 0.3027331 | -2.593494 | 0.0095006 | 0.6662747 |
| rno-let-7c-1-3p | 1.083702e+02 | 0.8569700 | 0.3596575 | 2.382740 | 0.0171843 | 0.6904457 |
| rno-let-7g-5p | 1.783876e+05 | -0.3332333 | 0.1608709 | -2.071434 | 0.0383183 | 0.6904457 |
| rno-let-7i-5p | 9.011407e+04 | -0.3231328 | 0.1379623 | -2.342181 | 0.0191714 | 0.6904457 |
| rno-miR-1224 | 2.037845e+03 | 0.8079795 | 0.3220219 | 2.509082 | 0.0121045 | 0.6904457 |
| rno-miR-184 | 5.673112e+01 | 0.8881593 | 0.4201132 | 2.114095 | 0.0345072 | 0.6904457 |
| rno-miR-217-5p | 9.701202e+01 | 0.9027633 | 0.3880746 | 2.326263 | 0.0200045 | 0.6904457 |
| rno-miR-221-3p | 1.397283e+04 | -0.5901842 | 0.2474779 | -2.384795 | 0.0170886 | 0.6904457 |
| rno-miR-26b-5p | 1.806391e+04 | -0.5732746 | 0.2475283 | -2.315996 | 0.0205585 | 0.6904457 |
| rno-miR-29b-5p | 2.134420e+02 | -0.6283511 | 0.2984915 | -2.105089 | 0.0352836 | 0.6904457 |
| rno-miR-300-3p | 4.027974e+03 | 0.5657890 | 0.2613362 | 2.164985 | 0.0303888 | 0.6904457 |
| rno-miR-30c-5p | 3.948778e+04 | -0.3584142 | 0.1605832 | -2.231954 | 0.0256180 | 0.6904457 |
| rno-miR-3550 | 9.825676e+00 | -0.6517760 | 0.3094926 | -2.105950 | 0.0352087 | 0.6904457 |
Too few genes to plot.
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 46, 8.6%
LFC < 0 (down) : 52, 9.7%
outliers [1] : 16, 3%
low counts [2] : 154, 29%
(mean count < 52)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-miR-22-3p | 8846.8517 | -1.4765625 | 0.2565138 | -5.756270 | 0.0000000 | 0.0000031 |
| rno-miR-153-3p | 1041.2503 | -1.7206478 | 0.3638884 | -4.728504 | 0.0000023 | 0.0004116 |
| rno-miR-24-3p | 12699.2038 | -1.3059476 | 0.2897157 | -4.507688 | 0.0000066 | 0.0006204 |
| rno-miR-664-2-5p | 2114.0938 | 1.2348988 | 0.2744640 | 4.499312 | 0.0000068 | 0.0006204 |
| rno-miR-486 | 10033.2396 | 1.0386413 | 0.2565516 | 4.048469 | 0.0000516 | 0.0037531 |
| rno-let-7c-1-3p | 108.3702 | 1.4222609 | 0.3610536 | 3.939196 | 0.0000818 | 0.0049598 |
| rno-miR-6331 | 10949.7887 | 0.9707109 | 0.2533241 | 3.831893 | 0.0001272 | 0.0066124 |
| rno-miR-30e-5p | 15160.1468 | -1.1091981 | 0.2966101 | -3.739583 | 0.0001843 | 0.0083868 |
| rno-miR-760-3p | 18118.8746 | 1.0088409 | 0.2757887 | 3.658021 | 0.0002542 | 0.0092518 |
| rno-miR-770-3p | 6259.4347 | 0.8519007 | 0.2318881 | 3.673758 | 0.0002390 | 0.0092518 |
| rno-let-7i-3p | 192.6834 | -1.3018100 | 0.3761113 | -3.461236 | 0.0005377 | 0.0095527 |
| rno-miR-126a-3p | 5099.5091 | -1.1536174 | 0.3219136 | -3.583625 | 0.0003389 | 0.0095527 |
| rno-miR-129-1-3p | 812.8703 | -0.9835758 | 0.2821115 | -3.486479 | 0.0004894 | 0.0095527 |
| rno-miR-129-5p | 77744.3042 | 0.7234014 | 0.2074140 | 3.487717 | 0.0004872 | 0.0095527 |
| rno-miR-181c-5p | 810.7187 | -1.2305556 | 0.3516598 | -3.499278 | 0.0004665 | 0.0095527 |
| rno-miR-21-5p | 6391.5568 | -1.1683660 | 0.3405922 | -3.430396 | 0.0006027 | 0.0095527 |
| rno-miR-27a-3p | 4586.8163 | -0.8221401 | 0.2393596 | -3.434749 | 0.0005931 | 0.0095527 |
| rno-miR-320-3p | 35929.5317 | 0.8732537 | 0.2463355 | 3.544978 | 0.0003926 | 0.0095527 |
| rno-miR-338-3p | 428.3078 | -1.5003333 | 0.4151098 | -3.614305 | 0.0003012 | 0.0095527 |
| rno-miR-346 | 9427.3911 | 0.7372020 | 0.2112424 | 3.489839 | 0.0004833 | 0.0095527 |
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 28, 5.2%
LFC < 0 (down) : 15, 2.8%
outliers [1] : 16, 3%
low counts [2] : 286, 54%
(mean count < 487)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-let-7b-3p | 1413.0698 | 1.0556344 | 0.2502823 | 4.217775 | 0.0000247 | 0.0057240 |
| rno-miR-486 | 10033.2396 | 1.0015054 | 0.2565550 | 3.903667 | 0.0000947 | 0.0109906 |
| rno-miR-22-3p | 8846.8517 | -0.9531994 | 0.2565346 | -3.715676 | 0.0002027 | 0.0117544 |
| rno-miR-328a-3p | 32650.9621 | 1.0080910 | 0.2684845 | 3.754746 | 0.0001735 | 0.0117544 |
| rno-miR-664-2-5p | 2114.0938 | 0.9915282 | 0.2744295 | 3.613053 | 0.0003026 | 0.0140413 |
| rno-miR-485-3p | 20270.1614 | 0.9823151 | 0.2959961 | 3.318676 | 0.0009045 | 0.0349722 |
| rno-miR-338-5p | 11017.5507 | 0.7739181 | 0.2393126 | 3.233921 | 0.0012210 | 0.0404685 |
| rno-miR-346 | 9427.3911 | 0.6714526 | 0.2112447 | 3.178554 | 0.0014801 | 0.0429234 |
| rno-miR-181c-5p | 810.7187 | -1.0537670 | 0.3517019 | -2.996193 | 0.0027337 | 0.0489578 |
| rno-miR-3068-3p | 841.0185 | -0.8107799 | 0.2689793 | -3.014283 | 0.0025759 | 0.0489578 |
| rno-miR-30e-5p | 15160.1468 | -0.9114086 | 0.2966145 | -3.072705 | 0.0021213 | 0.0489578 |
| rno-miR-6331 | 10949.7887 | 0.7587189 | 0.2533179 | 2.995125 | 0.0027433 | 0.0489578 |
| rno-miR-92b-3p | 144437.6875 | 0.8364236 | 0.2753091 | 3.038125 | 0.0023806 | 0.0489578 |
| rno-miR-134-5p | 7462.8354 | 0.6244672 | 0.2165469 | 2.883750 | 0.0039297 | 0.0514099 |
| rno-miR-136-3p | 6012.4037 | -0.8531977 | 0.2998238 | -2.845664 | 0.0044319 | 0.0514099 |
| rno-miR-3099 | 15618.3564 | 0.7954894 | 0.2742599 | 2.900495 | 0.0037257 | 0.0514099 |
| rno-miR-320-3p | 35929.5317 | 0.7042609 | 0.2463343 | 2.858965 | 0.0042503 | 0.0514099 |
| rno-miR-434-3p | 91232.6067 | 0.5604858 | 0.1937338 | 2.893072 | 0.0038149 | 0.0514099 |
| rno-miR-504 | 1486.8654 | 0.7584182 | 0.2635798 | 2.877376 | 0.0040100 | 0.0514099 |
| rno-miR-92a-3p | 17965.7867 | 0.5745431 | 0.2016019 | 2.849889 | 0.0043734 | 0.0514099 |
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 30, 5.6%
LFC < 0 (down) : 55, 10%
outliers [1] : 16, 3%
low counts [2] : 173, 32%
(mean count < 77)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-miR-22-3p | 8846.8517 | -1.8040773 | 0.2565293 | -7.032636 | 0.0000000 | 0.0000000 |
| rno-miR-153-3p | 1041.2503 | -1.8097504 | 0.3638827 | -4.973444 | 0.0000007 | 0.0001135 |
| rno-miR-7b | 64372.7263 | 0.6937716 | 0.1458424 | 4.756994 | 0.0000020 | 0.0002260 |
| rno-miR-24-3p | 12699.2038 | -1.3258405 | 0.2897126 | -4.576399 | 0.0000047 | 0.0003704 |
| rno-miR-341 | 722.4310 | -1.2927800 | 0.2841349 | -4.549881 | 0.0000054 | 0.0003704 |
| rno-miR-25-3p | 6589.1738 | 0.8217960 | 0.1854548 | 4.431246 | 0.0000094 | 0.0004040 |
| rno-miR-411-5p | 11719.3215 | -1.4049617 | 0.3151401 | -4.458213 | 0.0000083 | 0.0004040 |
| rno-miR-873-5p | 1224.4825 | -1.2221684 | 0.2730253 | -4.476393 | 0.0000076 | 0.0004040 |
| rno-miR-181c-5p | 810.7187 | -1.4800604 | 0.3517019 | -4.208282 | 0.0000257 | 0.0009864 |
| rno-miR-135b-5p | 1555.1704 | -1.3304313 | 0.3268895 | -4.069973 | 0.0000470 | 0.0014326 |
| rno-miR-136-3p | 6012.4037 | -1.2087930 | 0.2998345 | -4.031534 | 0.0000554 | 0.0014326 |
| rno-miR-143-3p | 60084.1480 | -1.1452277 | 0.2844980 | -4.025434 | 0.0000569 | 0.0014326 |
| rno-miR-300-3p | 4027.9744 | -1.0709119 | 0.2613054 | -4.098316 | 0.0000416 | 0.0014326 |
| rno-miR-330-5p | 3257.0945 | -0.7683431 | 0.1911179 | -4.020257 | 0.0000581 | 0.0014326 |
| rno-let-7g-5p | 178387.5920 | 0.6372825 | 0.1608731 | 3.961399 | 0.0000745 | 0.0017138 |
| rno-miR-221-3p | 13972.8278 | 0.9663747 | 0.2474958 | 3.904611 | 0.0000944 | 0.0018582 |
| rno-miR-29a-3p | 39082.9867 | -1.1456511 | 0.2918754 | -3.925138 | 0.0000867 | 0.0018582 |
| rno-miR-30e-5p | 15160.1468 | -1.1562090 | 0.2966084 | -3.898099 | 0.0000970 | 0.0018582 |
| rno-miR-376b-5p | 833.0608 | -1.3357972 | 0.3441307 | -3.881657 | 0.0001037 | 0.0018838 |
| rno-miR-338-5p | 11017.5507 | 0.8896595 | 0.2392935 | 3.717858 | 0.0002009 | 0.0034659 |
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 39, 7.3%
LFC < 0 (down) : 40, 7.5%
outliers [1] : 16, 3%
low counts [2] : 133, 25%
(mean count < 37)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-miR-22-3p | 8846.8517 | -1.2807142 | 0.2565501 | -4.992062 | 0.0000006 | 0.0002300 |
| rno-miR-25-3p | 6589.1738 | 0.8029125 | 0.1854654 | 4.329177 | 0.0000150 | 0.0014405 |
| rno-miR-378a-3p | 8126.9381 | 0.9625769 | 0.2161896 | 4.452466 | 0.0000085 | 0.0014405 |
| rno-miR-873-5p | 1224.4825 | -1.1946259 | 0.2730547 | -4.375043 | 0.0000121 | 0.0014405 |
| rno-miR-300-3p | 4027.9744 | -1.0858358 | 0.2613117 | -4.155328 | 0.0000325 | 0.0021736 |
| rno-miR-341 | 722.4310 | -1.1681811 | 0.2842106 | -4.110266 | 0.0000395 | 0.0021736 |
| rno-miR-34a-5p | 639.2179 | 1.2513718 | 0.3031441 | 4.127976 | 0.0000366 | 0.0021736 |
| rno-miR-338-5p | 11017.5507 | 0.9680163 | 0.2393039 | 4.045134 | 0.0000523 | 0.0025166 |
| rno-miR-136-3p | 6012.4037 | -1.2006552 | 0.2998383 | -4.004342 | 0.0000622 | 0.0026604 |
| rno-miR-148b-3p | 18916.3447 | 1.0531905 | 0.2704948 | 3.893571 | 0.0000988 | 0.0038030 |
| rno-miR-411-5p | 11719.3215 | -1.2135765 | 0.3151448 | -3.850853 | 0.0001177 | 0.0041198 |
| rno-miR-221-3p | 13972.8278 | 0.9393864 | 0.2474986 | 3.795522 | 0.0001473 | 0.0047269 |
| rno-miR-181c-5p | 810.7187 | -1.3032718 | 0.3517440 | -3.705172 | 0.0002112 | 0.0062562 |
| rno-miR-666-3p | 661.6309 | -1.1189440 | 0.3072364 | -3.641964 | 0.0002706 | 0.0074406 |
| rno-miR-127-5p | 3297.9653 | -0.9522423 | 0.2640810 | -3.605872 | 0.0003111 | 0.0079851 |
| rno-miR-135b-5p | 1555.1704 | -1.1345837 | 0.3269209 | -3.470515 | 0.0005195 | 0.0124995 |
| rno-miR-204-3p | 398.0936 | 1.4311151 | 0.4174082 | 3.428574 | 0.0006068 | 0.0129779 |
| rno-miR-30c-5p | 39487.7781 | 0.5510054 | 0.1605934 | 3.431058 | 0.0006012 | 0.0129779 |
| rno-miR-143-3p | 60084.1480 | -0.9700462 | 0.2844991 | -3.409663 | 0.0006504 | 0.0130768 |
| rno-miR-423-3p | 6952.3076 | -0.6961335 | 0.2048780 | -3.397794 | 0.0006793 | 0.0130768 |
out of 534 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 3, 0.56%
outliers [1] : 16, 3%
low counts [2] : 0, 0%
(mean count < 1)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-miR-3084a-3p | 173.25673 | -1.1572650 | 0.3161442 | -3.6605605 | 0.0002517 | 0.0434540 |
| rno-miR-3084b-3p | 173.25673 | -1.1572650 | 0.3161442 | -3.6605605 | 0.0002517 | 0.0434540 |
| rno-miR-3084d | 173.25673 | -1.1572650 | 0.3161442 | -3.6605605 | 0.0002517 | 0.0434540 |
| rno-miR-153-3p | 1041.25031 | 0.9080540 | 0.3637816 | 2.4961519 | 0.0125549 | 0.9487183 |
| rno-miR-199a-3p | 351.59681 | 0.7498872 | 0.2975879 | 2.5198848 | 0.0117393 | 0.9487183 |
| rno-miR-30b-5p | 1285.78775 | 0.6970177 | 0.2823074 | 2.4690023 | 0.0135490 | 0.9487183 |
| rno-miR-34a-5p | 639.21791 | 0.7405366 | 0.3033907 | 2.4408677 | 0.0146520 | 0.9487183 |
| rno-miR-374-5p | 777.71666 | 0.6744364 | 0.2582631 | 2.6114313 | 0.0090164 | 0.9487183 |
| rno-let-7a-1-3p | 474.11203 | 0.2803374 | 0.2698442 | 1.0388859 | 0.2988578 | 0.9995178 |
| rno-let-7a-2-3p | 9.99905 | -0.0316332 | 0.2870190 | -0.1102128 | 0.9122406 | 0.9995178 |
| rno-let-7a-5p | 343964.29023 | 0.0789358 | 0.2712879 | 0.2909669 | 0.7710766 | 0.9995178 |
| rno-let-7b-3p | 1413.06980 | 0.5210208 | 0.2504528 | 2.0803152 | 0.0374966 | 0.9995178 |
| rno-let-7b-5p | 147107.81338 | 0.0001925 | 0.3185200 | 0.0006044 | 0.9995178 | 0.9995178 |
| rno-let-7c-1-3p | 108.37018 | -0.2937907 | 0.3627436 | -0.8099129 | 0.4179902 | 0.9995178 |
| rno-let-7c-2-3p | 474.11203 | 0.2803374 | 0.2698442 | 1.0388859 | 0.2988578 | 0.9995178 |
| rno-let-7c-5p | 449579.50575 | 0.0845380 | 0.2929206 | 0.2886036 | 0.7728847 | 0.9995178 |
| rno-let-7d-3p | 15287.86071 | 0.2052951 | 0.2859169 | 0.7180237 | 0.4727427 | 0.9995178 |
| rno-let-7d-5p | 118683.06227 | -0.0579281 | 0.2270338 | -0.2551516 | 0.7986060 | 0.9995178 |
| rno-let-7e-3p | 296.06776 | 0.1365954 | 0.3131720 | 0.4361672 | 0.6627155 | 0.9995178 |
| rno-let-7e-5p | 120378.84152 | 0.0165671 | 0.2862553 | 0.0578751 | 0.9538481 | 0.9995178 |
Too few genes to plot.
> comp = c("IsoF_vs_GroupedF", "IsoM_vs_GroupedM")
> library(UpSetR)
> ma = do.call(rbind, lapply(comp, function(c) {
+ all_results[[c]] %>% as.data.frame() %>% tibble::rownames_to_column("id") %>%
+ filter(padj < 0.05) %>% select(id) %>% mutate(is_de = 1, comparison = c)
+ })) %>% tidyr::spread(key = "comparison", value = "is_de")
> ma[is.na(ma)] = 0
> upset(ma, sets = comp)> save_file(ma, paste0(comp[[1]], "_and_", comp[[2]], "_common.xls"), root_file)> counts = counts(obj_mirdeep)
> dss_mirdeep2 = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) >
+ 3, ], colData = design, design = formula)
>
> dss_mirdeep2 = DESeq(dss_mirdeep2)
> all_results = handle_deseq2(dss_mirdeep2, design, condition, "mirdeep2_")
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 0, 0%
outliers [1] : 55, 9.5%
low counts [2] : 0, 0%
(mean count < 1)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-3_16804-5p | 60.613974 | 2.0127451 | 0.5547742 | 3.6280435 | 0.0002856 | 0.1496425 |
| rno-1_1220-5p | 15435.818049 | -1.6065088 | 0.5154640 | -3.1166265 | 0.0018293 | 0.1992851 |
| rno-17_11192-5p | 78.587529 | 1.3942817 | 0.4416207 | 3.1571931 | 0.0015930 | 0.1992851 |
| rno-20_15692-5p | 96.663633 | 1.7480719 | 0.5629519 | 3.1051889 | 0.0019016 | 0.1992851 |
| rno-X_27646-5p | 5350.226331 | -1.9431633 | 0.5816736 | -3.3406422 | 0.0008358 | 0.1992851 |
| rno-4_18441-5p | 43.437632 | 1.7401690 | 0.5797818 | 3.0014203 | 0.0026872 | 0.2346851 |
| rno-X_27644-5p | 1060.419728 | -0.9293440 | 0.3726143 | -2.4941179 | 0.0126271 | 0.9452255 |
| rno-1_102-5p | 2.960714 | 0.0000000 | 0.3249113 | 0.0000000 | 1.0000000 | 1.0000000 |
| rno-1_1103-5p | 7.221976 | 0.1393850 | 0.4213094 | 0.3308376 | 0.7407672 | 1.0000000 |
| rno-1_1111-5p | 6.103605 | 0.0286342 | 0.4243939 | 0.0674708 | 0.9462069 | 1.0000000 |
| rno-1_1115-5p | 284.114415 | 0.4863109 | 0.4074055 | 1.1936777 | 0.2326041 | 1.0000000 |
| rno-1_1121-5p | 6.768583 | -0.2908358 | 0.4589289 | -0.6337274 | 0.5262587 | 1.0000000 |
| rno-1_1174-5p | 348.723241 | -0.9521161 | 0.4469098 | -2.1304437 | 0.0331350 | 1.0000000 |
| rno-1_1203-3p | 4.791731 | 0.3988375 | 0.3606156 | 1.1059907 | 0.2687305 | 1.0000000 |
| rno-1_1203-5p | 18.350014 | 0.5375388 | 0.5650695 | 0.9512791 | 0.3414627 | 1.0000000 |
| rno-1_1220-3p | 391.139616 | -0.9459230 | 0.4971734 | -1.9026017 | 0.0570925 | 1.0000000 |
| rno-1_1231-5p | 6.083585 | -0.1651937 | 0.4034095 | -0.4094938 | 0.6821773 | 1.0000000 |
| rno-1_1516-5p | 23.652852 | 0.8034698 | 0.5991308 | 1.3410590 | 0.1799013 | 1.0000000 |
| rno-1_1611-5p | 533.518164 | -0.0315915 | 0.3188502 | -0.0990793 | 0.9210753 | 1.0000000 |
| rno-1_1688-5p | 1.559850 | -0.1784906 | 0.3233222 | -0.5520519 | 0.5809128 | 1.0000000 |
Too few genes to plot.
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 8, 1.4%
LFC < 0 (down) : 8, 1.4%
outliers [1] : 55, 9.5%
low counts [2] : 385, 66%
(mean count < 51)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-19_12349-5p | 13015.97889 | -1.3973626 | 0.3322258 | -4.206062 | 0.0000260 | 0.0036120 |
| rno-6_21996-5p | 6841.14075 | 0.9912611 | 0.2716406 | 3.649164 | 0.0002631 | 0.0182851 |
| rno-19_12701-5p | 830.81407 | -1.3611481 | 0.4068017 | -3.345974 | 0.0008199 | 0.0360161 |
| rno-7_23643-3p | 1201.46978 | 0.8928136 | 0.2775328 | 3.216966 | 0.0012955 | 0.0360161 |
| rno-9_26461-5p | 1012.53231 | 0.8071652 | 0.2485155 | 3.247947 | 0.0011624 | 0.0360161 |
| rno-7_23643-5p | 6854.30765 | -1.0547138 | 0.3437849 | -3.067947 | 0.0021554 | 0.0499323 |
| rno-10_3415-3p | 1547.51485 | -0.8556939 | 0.2930023 | -2.920434 | 0.0034954 | 0.0600720 |
| rno-17_10819-5p | 242.62398 | 1.0620411 | 0.3593980 | 2.955055 | 0.0031261 | 0.0600720 |
| rno-X_27957-5p | 188.15687 | 1.2112851 | 0.4195678 | 2.886983 | 0.0038896 | 0.0600720 |
| rno-1_1732-5p | 15316.75518 | 0.7437878 | 0.2700529 | 2.754230 | 0.0058830 | 0.0720827 |
| rno-1_2845-5p | 684.99211 | 0.9352891 | 0.3514107 | 2.661527 | 0.0077787 | 0.0720827 |
| rno-19_12349-3p | 635.39805 | -1.0772583 | 0.3877574 | -2.778176 | 0.0054665 | 0.0720827 |
| rno-5_19862-5p | 382.28627 | -1.3210323 | 0.4948825 | -2.669386 | 0.0075990 | 0.0720827 |
| rno-6_21996-3p | 291.80109 | -1.0507076 | 0.3921724 | -2.679198 | 0.0073799 | 0.0720827 |
| rno-X_27286-5p | 56104.98543 | 0.6392423 | 0.2395538 | 2.668471 | 0.0076197 | 0.0720827 |
| rno-18_12068-5p | 61350.60992 | -0.8279590 | 0.3247424 | -2.549587 | 0.0107851 | 0.0936952 |
| rno-3_17696-5p | 1955.81156 | 0.7192623 | 0.2878395 | 2.498831 | 0.0124604 | 0.1018819 |
| rno-20_15478-3p | 64.08061 | -1.4631103 | 0.6003411 | -2.437132 | 0.0148043 | 0.1143220 |
| rno-5_20150-5p | 376.17285 | 0.7764214 | 0.3302285 | 2.351164 | 0.0187148 | 0.1369134 |
| rno-10_3414-3p | 1322.41265 | -0.5255523 | 0.2294348 | -2.290639 | 0.0219843 | 0.1527908 |
Too few genes to plot.
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1, 0.17%
LFC < 0 (down) : 0, 0%
outliers [1] : 55, 9.5%
low counts [2] : 0, 0%
(mean count < 1)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-4_18441-5p | 43.43763 | 2.4770928 | 0.5815292 | 4.259619 | 0.0000205 | 0.0107302 |
| rno-1_1732-5p | 15316.75518 | 0.7706224 | 0.2700569 | 2.853555 | 0.0043233 | 0.5363856 |
| rno-19_12701-5p | 830.81407 | -1.1333856 | 0.4068597 | -2.785692 | 0.0053414 | 0.5363856 |
| rno-3_16685-5p | 52.09258 | 1.4748822 | 0.5311285 | 2.776884 | 0.0054883 | 0.5363856 |
| rno-4_17822-5p | 66.18588 | 1.3758787 | 0.5021249 | 2.740112 | 0.0061418 | 0.5363856 |
| rno-X_27957-5p | 188.15687 | 1.1505527 | 0.4195863 | 2.742112 | 0.0061046 | 0.5363856 |
| rno-6_21996-5p | 6841.14075 | 0.7296201 | 0.2716226 | 2.686154 | 0.0072280 | 0.5410655 |
| rno-17_10819-5p | 242.62398 | 0.8757341 | 0.3591963 | 2.438038 | 0.0147672 | 0.7476631 |
| rno-17_11185-5p | 98.30355 | 1.1800182 | 0.4761237 | 2.478386 | 0.0131978 | 0.7476631 |
| rno-19_12349-5p | 13015.97889 | -0.8058006 | 0.3322392 | -2.425363 | 0.0152931 | 0.7476631 |
| rno-6_22111-5p | 21.30099 | 1.3981409 | 0.5847530 | 2.390994 | 0.0168028 | 0.7476631 |
| rno-X_27286-5p | 56104.98543 | 0.5711147 | 0.2395539 | 2.384076 | 0.0171221 | 0.7476631 |
| rno-1_2845-5p | 684.99211 | 0.7978670 | 0.3513733 | 2.270711 | 0.0231645 | 0.7783711 |
| rno-3_17696-5p | 1955.81156 | 0.6650361 | 0.2878448 | 2.310398 | 0.0208661 | 0.7783711 |
| rno-6_21889-3p | 1602.40820 | -0.7168524 | 0.3131229 | -2.289364 | 0.0220582 | 0.7783711 |
| rno-7_23361-5p | 694000.92931 | 0.6285555 | 0.2809164 | 2.237518 | 0.0252525 | 0.7783711 |
| rno-7_23643-5p | 6854.30765 | -0.7711432 | 0.3437982 | -2.243011 | 0.0248961 | 0.7783711 |
| rno-10_3509-5p | 7548.20370 | 0.5036309 | 0.2333087 | 2.158646 | 0.0308776 | 0.8089937 |
| rno-4_18916-5p | 18.42630 | -1.2603593 | 0.5786320 | -2.178171 | 0.0293933 | 0.8089937 |
| rno-7_23643-3p | 1201.46978 | 0.6047946 | 0.2774192 | 2.180075 | 0.0292519 | 0.8089937 |
Too few genes to plot.
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 6, 1%
LFC < 0 (down) : 15, 2.6%
outliers [1] : 55, 9.5%
low counts [2] : 373, 64%
(mean count < 46)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-6_21996-3p | 291.80109 | -1.9496369 | 0.3931541 | -4.958963 | 0.0000007 | 0.0001070 |
| rno-19_12349-5p | 13015.97889 | -1.4625695 | 0.3322244 | -4.402354 | 0.0000107 | 0.0008085 |
| rno-19_12701-5p | 830.81407 | -1.6852244 | 0.4068918 | -4.141701 | 0.0000345 | 0.0017352 |
| rno-18_12068-5p | 61350.60992 | -1.2595937 | 0.3247447 | -3.878720 | 0.0001050 | 0.0039640 |
| rno-7_23643-3p | 1201.46978 | 0.9788299 | 0.2774851 | 3.527504 | 0.0004195 | 0.0105573 |
| rno-X_27201-5p | 14118.79845 | 0.9627085 | 0.2706674 | 3.556795 | 0.0003754 | 0.0105573 |
| rno-6_21904-5p | 2686.11929 | -0.9393792 | 0.2760491 | -3.402942 | 0.0006666 | 0.0143805 |
| rno-19_12349-3p | 635.39805 | -1.2495818 | 0.3877897 | -3.222318 | 0.0012716 | 0.0240011 |
| rno-6_21996-5p | 6841.14075 | 0.8488178 | 0.2716423 | 3.124763 | 0.0017795 | 0.0298558 |
| rno-10_3509-5p | 7548.20370 | 0.7034889 | 0.2332476 | 3.016061 | 0.0025608 | 0.0386683 |
| rno-10_3415-3p | 1547.51485 | -0.8412042 | 0.2929666 | -2.871331 | 0.0040875 | 0.0538808 |
| rno-6_21879-5p | 309.60427 | -1.3732536 | 0.4807284 | -2.856610 | 0.0042819 | 0.0538808 |
| rno-17_10819-5p | 242.62398 | 0.9691015 | 0.3593876 | 2.696536 | 0.0070065 | 0.0679533 |
| rno-2_13235-5p | 2139.18939 | -0.5679957 | 0.2093419 | -2.713244 | 0.0066628 | 0.0679533 |
| rno-7_23643-5p | 6854.30765 | -0.9395382 | 0.3437729 | -2.733020 | 0.0062757 | 0.0679533 |
| rno-X_27286-5p | 56104.98543 | 0.6437829 | 0.2395531 | 2.687433 | 0.0072003 | 0.0679533 |
| rno-3_16804-5p | 60.61397 | -1.4576996 | 0.5553025 | -2.625055 | 0.0086635 | 0.0769522 |
| rno-7_22962-5p | 608.04064 | -0.6645320 | 0.2560094 | -2.595733 | 0.0094389 | 0.0791822 |
| rno-5_19862-5p | 382.28627 | -1.2586247 | 0.4948211 | -2.543595 | 0.0109718 | 0.0871970 |
| rno-4_18657-5p | 142.46031 | -1.3741785 | 0.5568896 | -2.467596 | 0.0136024 | 0.0978076 |
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 14, 2.4%
LFC < 0 (down) : 12, 2.1%
outliers [1] : 55, 9.5%
low counts [2] : 396, 68%
(mean count < 60)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-10_3509-5p | 7548.20370 | 0.9450574 | 0.2332792 | 4.051185 | 0.0000510 | 0.0055765 |
| rno-6_21996-3p | 291.80109 | -1.5438534 | 0.3934501 | -3.923886 | 0.0000871 | 0.0055765 |
| rno-19_12701-5p | 830.81407 | -1.4574619 | 0.4069498 | -3.581429 | 0.0003417 | 0.0145800 |
| rno-X_27201-5p | 14118.79845 | 0.9331696 | 0.2706694 | 3.447636 | 0.0005655 | 0.0180965 |
| rno-1_1220-3p | 391.13962 | 1.6515322 | 0.4975784 | 3.319140 | 0.0009030 | 0.0192630 |
| rno-4_17822-5p | 66.18588 | 1.6798761 | 0.5016641 | 3.348607 | 0.0008122 | 0.0192630 |
| rno-18_12068-5p | 61350.60992 | -1.0576891 | 0.3247459 | -3.256974 | 0.0011261 | 0.0205909 |
| rno-1_1220-5p | 15435.81805 | 1.5383692 | 0.5154662 | 2.984423 | 0.0028411 | 0.0404073 |
| rno-6_21904-5p | 2686.11929 | -0.8320028 | 0.2760749 | -3.013685 | 0.0025810 | 0.0404073 |
| rno-6_21879-5p | 309.60427 | -1.3817793 | 0.4807496 | -2.874218 | 0.0040503 | 0.0518437 |
| rno-6_21889-3p | 1602.40820 | -0.8830082 | 0.3131382 | -2.819867 | 0.0048044 | 0.0559052 |
| rno-X_27646-5p | 5350.22633 | 1.6228623 | 0.5816732 | 2.789990 | 0.0052710 | 0.0562237 |
| rno-11_5095-5p | 2148.81476 | -0.8270102 | 0.3176958 | -2.603152 | 0.0092371 | 0.0735088 |
| rno-19_12349-5p | 13015.97889 | -0.8710076 | 0.3322377 | -2.621640 | 0.0087508 | 0.0735088 |
| rno-5_20919-5p | 39613.81553 | 0.5125353 | 0.1963699 | 2.610050 | 0.0090529 | 0.0735088 |
| rno-6_21953-5p | 33250.79270 | -0.6124120 | 0.2357347 | -2.597886 | 0.0093800 | 0.0735088 |
| rno-6_22744-5p | 166.19960 | 1.0013738 | 0.3875111 | 2.584116 | 0.0097629 | 0.0735088 |
| rno-3_15933-5p | 353.23835 | 0.8566213 | 0.3344108 | 2.561584 | 0.0104196 | 0.0740949 |
| rno-7_23643-3p | 1201.46978 | 0.6908109 | 0.2773714 | 2.490563 | 0.0127541 | 0.0859223 |
| rno-X_27199-5p | 24321.98450 | 0.5620012 | 0.2305203 | 2.437969 | 0.0147701 | 0.0945283 |
Too few genes to plot.
out of 579 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 0, 0%
outliers [1] : 55, 9.5%
low counts [2] : 0, 0%
(mean count < 1)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| rno-1_102-5p | 2.960714 | -0.2709370 | 0.3248093 | -0.8341418 | 0.4042011 | 1 |
| rno-1_1103-5p | 7.221976 | 0.0268353 | 0.4202972 | 0.0638485 | 0.9490909 | 1 |
| rno-1_1111-5p | 6.103605 | -0.0855297 | 0.4239279 | -0.2017552 | 0.8401081 | 1 |
| rno-1_1115-5p | 284.114415 | -0.0765270 | 0.4072121 | -0.1879292 | 0.8509321 | 1 |
| rno-1_1121-5p | 6.768583 | -0.1542935 | 0.4587245 | -0.3363534 | 0.7366044 | 1 |
| rno-1_1174-5p | 348.723241 | 0.1039706 | 0.4473285 | 0.2324257 | 0.8162074 | 1 |
| rno-1_1203-3p | 4.791731 | -0.0261316 | 0.3600610 | -0.0725754 | 0.9421440 | 1 |
| rno-1_1203-5p | 18.350014 | -0.3169132 | 0.5641842 | -0.5617194 | 0.5743072 | 1 |
| rno-1_1220-3p | 391.139616 | 0.5925099 | 0.4978943 | 1.1900314 | 0.2340340 | 1 |
| rno-1_1220-5p | 15435.818049 | 0.6944793 | 0.5154713 | 1.3472707 | 0.1778931 | 1 |
| rno-1_1231-5p | 6.083585 | -0.0823751 | 0.4023672 | -0.2047262 | 0.8377861 | 1 |
| rno-1_1516-5p | 23.652852 | -0.0871916 | 0.5988698 | -0.1455936 | 0.8842422 | 1 |
| rno-1_1611-5p | 533.518164 | -0.0970397 | 0.3195683 | -0.3036586 | 0.7613880 | 1 |
| rno-1_1688-5p | 1.559850 | 0.1058899 | 0.3226085 | 0.3282303 | 0.7427375 | 1 |
| rno-1_1704-5p | 23.500861 | 0.0147710 | 0.6000002 | 0.0246183 | 0.9803594 | 1 |
| rno-1_1732-5p | 15316.755180 | 0.0268346 | 0.2700838 | 0.0993565 | 0.9208553 | 1 |
| rno-1_1761-5p | 8.022479 | 0.1855836 | 0.4175375 | 0.4444717 | 0.6567016 | 1 |
| rno-1_1796-5p | 5.088036 | -0.1309974 | 0.3674284 | -0.3565250 | 0.7214474 | 1 |
| rno-1_1822-5p | 4.394664 | 0.1578577 | 0.3805154 | 0.4148523 | 0.6782500 | 1 |
| rno-1_1829-3p | 14.683238 | 0.3513751 | 0.5452945 | 0.6443768 | 0.5193311 | 1 |
Too few genes to plot.
> dss_clus = DESeqDataSetFromMatrix(countData = clus_ma[rowSums(clus_ma > 0) >
+ 3, ], colData = design, design = formula)
>
> dss_clus = DESeq(dss_clus)
> all_results = handle_deseq2(dss_clus, design, condition, "clusters_")
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 7, 0.96%
LFC < 0 (down) : 7, 0.96%
outliers [1] : 14, 1.9%
low counts [2] : 0, 0%
(mean count < 3)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 207 | 3058.56849 | 2.6576475 | 0.3683625 | 7.214761 | 0.0000000 | 0.0000000 |
| 103 | 8569.84911 | -2.3853570 | 0.4119391 | -5.790558 | 0.0000000 | 0.0000025 |
| 545 | 3901.07187 | -2.1574354 | 0.4232661 | -5.097114 | 0.0000003 | 0.0000820 |
| 208 | 2491.04303 | 1.9884943 | 0.4281022 | 4.644905 | 0.0000034 | 0.0006065 |
| 619 | 12010.68412 | 1.8137863 | 0.4631743 | 3.915991 | 0.0000900 | 0.0128388 |
| 449 | 855.03561 | 1.5302127 | 0.3994800 | 3.830512 | 0.0001279 | 0.0130252 |
| 498 | 52.96383 | 1.7630737 | 0.4580536 | 3.849055 | 0.0001186 | 0.0130252 |
| 712 | 9309.09852 | -1.6178452 | 0.4494871 | -3.599314 | 0.0003191 | 0.0284360 |
| 519 | 8809.65222 | -0.8227121 | 0.2317386 | -3.550173 | 0.0003850 | 0.0304988 |
| 286 | 1398.07792 | 0.9377873 | 0.2691603 | 3.484122 | 0.0004938 | 0.0352047 |
| 435 | 92.30506 | 1.6171064 | 0.4700581 | 3.440226 | 0.0005812 | 0.0376741 |
| 312 | 15444.25033 | -1.4791395 | 0.4350152 | -3.400202 | 0.0006734 | 0.0400089 |
| 126 | 8465.59998 | -1.2920971 | 0.4101550 | -3.150266 | 0.0016312 | 0.0894662 |
| 427 | 476.22626 | -1.2238398 | 0.3927742 | -3.115886 | 0.0018339 | 0.0933994 |
| 210 | 73.90093 | 1.0587341 | 0.3639640 | 2.908898 | 0.0036271 | 0.1724060 |
| 621 | 173.41672 | 0.9072869 | 0.3244259 | 2.796592 | 0.0051645 | 0.2301416 |
| 83 | 231.79795 | -0.7559813 | 0.2813340 | -2.687130 | 0.0072069 | 0.3022650 |
| 320 | 2134.95096 | 1.2226110 | 0.4586616 | 2.665606 | 0.0076850 | 0.3044106 |
| 252 | 1527.35067 | 0.5166857 | 0.1985703 | 2.602029 | 0.0092674 | 0.3477711 |
| 269 | 1880.28597 | 0.7998362 | 0.3111445 | 2.570626 | 0.0101515 | 0.3618570 |
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 63, 8.7%
LFC < 0 (down) : 74, 10%
outliers [1] : 14, 1.9%
low counts [2] : 99, 14%
(mean count < 87)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 629 | 20558.8614 | -2.6173103 | 0.4165328 | -6.283563 | 0.0000000 | 0.0000002 |
| 545 | 3901.0719 | -2.4436510 | 0.4232649 | -5.773338 | 0.0000000 | 0.0000024 |
| 207 | 3058.5685 | 1.7430553 | 0.3682317 | 4.733583 | 0.0000022 | 0.0003524 |
| 208 | 2491.0430 | 2.0230622 | 0.4281171 | 4.725487 | 0.0000023 | 0.0003524 |
| 127 | 7958.6790 | -1.8874773 | 0.4187405 | -4.507511 | 0.0000066 | 0.0006712 |
| 602 | 3589.1635 | -1.3494974 | 0.2979065 | -4.529935 | 0.0000059 | 0.0006712 |
| 329 | 8929.4766 | -1.3401215 | 0.3020064 | -4.437395 | 0.0000091 | 0.0006988 |
| 473 | 448.3449 | -1.5829621 | 0.3549743 | -4.459371 | 0.0000082 | 0.0006988 |
| 94 | 6110.9924 | 1.0706854 | 0.2580069 | 4.149833 | 0.0000333 | 0.0022699 |
| 605 | 3007.5046 | 1.0905064 | 0.2645853 | 4.121569 | 0.0000376 | 0.0023105 |
| 596 | 1363.3251 | -1.5702164 | 0.3907436 | -4.018534 | 0.0000586 | 0.0032688 |
| 619 | 12010.6841 | 1.8489508 | 0.4631759 | 3.991898 | 0.0000655 | 0.0033538 |
| 225 | 1158.7987 | -0.9590532 | 0.2463377 | -3.893246 | 0.0000989 | 0.0046717 |
| 274 | 17136.7679 | 1.0246892 | 0.2660643 | 3.851285 | 0.0001175 | 0.0048097 |
| 658 | 1100.5786 | -1.5598428 | 0.4042753 | -3.858368 | 0.0001141 | 0.0048097 |
| 139 | 226.1385 | 1.1629925 | 0.3087429 | 3.766864 | 0.0001653 | 0.0063438 |
| 662 | 523.6577 | -1.0546264 | 0.2819323 | -3.740707 | 0.0001835 | 0.0066277 |
| 356 | 2071.1666 | 0.8891185 | 0.2408057 | 3.692265 | 0.0002223 | 0.0075817 |
| 2 | 753.1373 | -1.4224813 | 0.3877157 | -3.668877 | 0.0002436 | 0.0078727 |
| 386 | 1123.6423 | -1.1385854 | 0.3126142 | -3.642142 | 0.0002704 | 0.0082503 |
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 17, 2.3%
LFC < 0 (down) : 9, 1.2%
outliers [1] : 14, 1.9%
low counts [2] : 99, 14%
(mean count < 87)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 208 | 2491.0430 | 2.1002420 | 0.4281311 | 4.905605 | 0.0000009 | 0.0005719 |
| 166 | 14038.4933 | 0.7753196 | 0.1904535 | 4.070913 | 0.0000468 | 0.0143766 |
| 313 | 263.2181 | 1.1937976 | 0.3072135 | 3.885889 | 0.0001020 | 0.0208670 |
| 621 | 173.4167 | 1.2403657 | 0.3256886 | 3.808441 | 0.0001398 | 0.0214663 |
| 65 | 31678.8339 | 0.9717231 | 0.2635460 | 3.687110 | 0.0002268 | 0.0278529 |
| 94 | 6110.9924 | 0.9089682 | 0.2580027 | 3.523096 | 0.0004265 | 0.0399572 |
| 261 | 5469.4331 | -1.5451212 | 0.4412070 | -3.502032 | 0.0004617 | 0.0399572 |
| 537 | 549.1650 | -1.3226572 | 0.3811781 | -3.469919 | 0.0005206 | 0.0399572 |
| 712 | 9309.0985 | -1.5438206 | 0.4494887 | -3.434615 | 0.0005934 | 0.0404828 |
| 229 | 1260.8551 | 0.8341389 | 0.2475311 | 3.369835 | 0.0007521 | 0.0461809 |
| 605 | 3007.5046 | 0.8741957 | 0.2645661 | 3.304261 | 0.0009523 | 0.0531540 |
| 26 | 267.7878 | 0.9310954 | 0.2901853 | 3.208624 | 0.0013337 | 0.0629925 |
| 513 | 8365.4522 | -0.9083170 | 0.2820579 | -3.220321 | 0.0012805 | 0.0629925 |
| 360 | 1886.6782 | 1.0111508 | 0.3190598 | 3.169158 | 0.0015288 | 0.0670494 |
| 256 | 2200.9034 | 0.8410704 | 0.2721481 | 3.090489 | 0.0019983 | 0.0817961 |
| 473 | 448.3449 | -1.0858971 | 0.3551842 | -3.057279 | 0.0022336 | 0.0846288 |
| 602 | 3589.1635 | -0.9066151 | 0.2979449 | -3.042895 | 0.0023431 | 0.0846288 |
| 108 | 1415.7342 | 0.8009044 | 0.2725690 | 2.938355 | 0.0032996 | 0.0960773 |
| 495 | 972.7800 | -1.0075894 | 0.3444531 | -2.925186 | 0.0034425 | 0.0960773 |
| 555 | 149.2049 | 0.9516509 | 0.3210293 | 2.964374 | 0.0030330 | 0.0960773 |
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 37, 5.1%
LFC < 0 (down) : 51, 7%
outliers [1] : 14, 1.9%
low counts [2] : 71, 9.8%
(mean count < 61)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 629 | 20558.8614 | -3.1026640 | 0.4165380 | -7.448693 | 0.0000000 | 0.0000000 |
| 329 | 8929.4766 | -1.6797971 | 0.3020179 | -5.561912 | 0.0000000 | 0.0000086 |
| 103 | 8569.8491 | 2.2314216 | 0.4119370 | 5.416900 | 0.0000001 | 0.0000130 |
| 136 | 1458.2023 | -2.1574393 | 0.4765312 | -4.527383 | 0.0000060 | 0.0009585 |
| 305 | 4195.9326 | -1.0014024 | 0.2253525 | -4.443717 | 0.0000088 | 0.0011353 |
| 32 | 965.7966 | -1.3114138 | 0.3367859 | -3.893909 | 0.0000986 | 0.0059217 |
| 79 | 7100.0995 | 0.8707344 | 0.2204321 | 3.950126 | 0.0000781 | 0.0059217 |
| 286 | 1398.0779 | -1.0462192 | 0.2691538 | -3.887068 | 0.0001015 | 0.0059217 |
| 473 | 448.3449 | -1.3806813 | 0.3547470 | -3.892017 | 0.0000994 | 0.0059217 |
| 602 | 3589.1635 | -1.1971542 | 0.2978724 | -4.019017 | 0.0000584 | 0.0059217 |
| 658 | 1100.5786 | -1.5844541 | 0.4042584 | -3.919409 | 0.0000888 | 0.0059217 |
| 126 | 8465.6000 | 1.5854536 | 0.4101619 | 3.865434 | 0.0001109 | 0.0059327 |
| 636 | 64424.7037 | 0.7705423 | 0.2059897 | 3.740684 | 0.0001835 | 0.0090631 |
| 302 | 6957.3939 | -0.7927753 | 0.2195533 | -3.610856 | 0.0003052 | 0.0130620 |
| 303 | 14868.4501 | -1.2126102 | 0.3354090 | -3.615318 | 0.0003000 | 0.0130620 |
| 31 | 1769.7581 | 1.0323124 | 0.3038582 | 3.397349 | 0.0006804 | 0.0233726 |
| 225 | 1158.7987 | -0.8353935 | 0.2462223 | -3.392843 | 0.0006917 | 0.0233726 |
| 327 | 14808.3550 | 1.0003556 | 0.2921382 | 3.424255 | 0.0006165 | 0.0233726 |
| 654 | 19045.2195 | 1.0794082 | 0.3165899 | 3.409484 | 0.0006509 | 0.0233726 |
| 643 | 1552.9238 | -1.2267738 | 0.3660835 | -3.351077 | 0.0008050 | 0.0258399 |
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 22, 3%
LFC < 0 (down) : 18, 2.5%
outliers [1] : 14, 1.9%
low counts [2] : 126, 17%
(mean count < 113)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 207 | 3058.5685 | -2.9723455 | 0.3683580 | -8.069176 | 0.0000000 | 0.0000000 |
| 545 | 3901.0719 | 2.0118037 | 0.4232701 | 4.753002 | 0.0000020 | 0.0005882 |
| 103 | 8569.8491 | 1.9133230 | 0.4119317 | 4.644757 | 0.0000034 | 0.0006662 |
| 126 | 8465.6000 | 1.6674230 | 0.4101664 | 4.065236 | 0.0000480 | 0.0055995 |
| 286 | 1398.0779 | -1.0967903 | 0.2691698 | -4.074715 | 0.0000461 | 0.0055995 |
| 329 | 8929.4766 | -1.2043567 | 0.3020332 | -3.987497 | 0.0000668 | 0.0055995 |
| 619 | 12010.6841 | -1.8500039 | 0.4631746 | -3.994183 | 0.0000649 | 0.0055995 |
| 193 | 1279.2023 | -1.7100628 | 0.4551962 | -3.756759 | 0.0001721 | 0.0126299 |
| 79 | 7100.0995 | 0.8129710 | 0.2204381 | 3.687979 | 0.0002260 | 0.0147430 |
| 32 | 965.7966 | -1.2270654 | 0.3368231 | -3.643055 | 0.0002694 | 0.0158150 |
| 135 | 7911.7134 | 0.9532908 | 0.2659458 | 3.584531 | 0.0003377 | 0.0180201 |
| 284 | 624.4886 | 1.1910512 | 0.3394241 | 3.509035 | 0.0004497 | 0.0219996 |
| 312 | 15444.2503 | 1.5000698 | 0.4350169 | 3.448302 | 0.0005641 | 0.0253935 |
| 513 | 8365.4522 | -0.9573568 | 0.2820538 | -3.394235 | 0.0006882 | 0.0253935 |
| 654 | 19045.2195 | 1.0740919 | 0.3165921 | 3.392668 | 0.0006922 | 0.0253935 |
| 659 | 11420.5260 | 0.9073688 | 0.2656198 | 3.416044 | 0.0006354 | 0.0253935 |
| 519 | 8809.6522 | 0.7814220 | 0.2317499 | 3.371833 | 0.0007467 | 0.0257830 |
| 325 | 4536.1945 | -0.9707318 | 0.2936628 | -3.305600 | 0.0009477 | 0.0309066 |
| 71 | 1094.1539 | 0.7885157 | 0.2399740 | 3.285838 | 0.0010168 | 0.0314136 |
| 557 | 6676.2565 | -1.1061113 | 0.3560940 | -3.106234 | 0.0018949 | 0.0556143 |
out of 727 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 8, 1.1%
LFC < 0 (down) : 5, 0.69%
outliers [1] : 14, 1.9%
low counts [2] : 126, 17%
(mean count < 113)
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| 629 | 20558.8614 | 2.8900699 | 0.4165399 | 6.938279 | 0.0000000 | 0.0000000 |
| 207 | 3058.5685 | -2.0577533 | 0.3682272 | -5.588271 | 0.0000000 | 0.0000067 |
| 545 | 3901.0719 | 2.2980194 | 0.4232689 | 5.429219 | 0.0000001 | 0.0000111 |
| 619 | 12010.6841 | -1.8851685 | 0.4631763 | -4.070089 | 0.0000470 | 0.0068966 |
| 127 | 7958.6790 | 1.6607874 | 0.4187398 | 3.966156 | 0.0000730 | 0.0085750 |
| 136 | 1458.2023 | 1.8658608 | 0.4765317 | 3.915502 | 0.0000902 | 0.0088262 |
| 266 | 1411.3785 | 0.7773177 | 0.2263519 | 3.434112 | 0.0005945 | 0.0498529 |
| 245 | 2102.8942 | -1.4948963 | 0.4410655 | -3.389284 | 0.0007008 | 0.0514178 |
| 690 | 332.9943 | 1.0985981 | 0.3301584 | 3.327488 | 0.0008763 | 0.0571561 |
| 261 | 5469.4331 | -1.4520485 | 0.4412068 | -3.291084 | 0.0009980 | 0.0585838 |
| 530 | 241.7596 | 0.9554591 | 0.2994857 | 3.190333 | 0.0014211 | 0.0758345 |
| 295 | 1884.3821 | -1.3205121 | 0.4313693 | -3.061210 | 0.0022044 | 0.0995391 |
| 529 | 1967.2551 | 1.3586299 | 0.4420277 | 3.073631 | 0.0021147 | 0.0995391 |
| 132 | 815.4345 | 0.7356690 | 0.2472900 | 2.974924 | 0.0029306 | 0.1228765 |
| 323 | 301.5834 | 1.2275504 | 0.4207314 | 2.917658 | 0.0035267 | 0.1293860 |
| 388 | 1099.3741 | 0.5933093 | 0.2024719 | 2.930329 | 0.0033860 | 0.1293860 |
| 40 | 997.8665 | 0.8247303 | 0.3045742 | 2.707814 | 0.0067728 | 0.2208685 |
| 481 | 471.8432 | 1.0297556 | 0.3786723 | 2.719384 | 0.0065404 | 0.2208685 |
| 346 | 3299.6572 | 1.2172760 | 0.4597789 | 2.647525 | 0.0081083 | 0.2505051 |
| 300 | 5644.7244 | -1.1225226 | 0.4316257 | -2.600685 | 0.0093038 | 0.2692978 |
Too few genes to plot.
Files description:
mirna__* files contain information about miRNA from miRBase, clusters__* about general small RNA clusters, and mirdeep2__* about novel miRNA discovery.
*log_matrix.txt is log2 normalized counts, *norm_matrix,txt is normalized count data, *raw_matrix.txt is raw count data, and *.tsv contains the DE analysis results with the log2FC, pvalue and padjust information.
(useful if replicating these results)
> sessionInfo()R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.12.1 (Sierra)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] vsn_3.40.0 edgeR_3.14.0
[3] limma_3.28.21 pheatmap_1.0.8
[5] isomiRs_1.1.4 DiscriMiner_0.1-29
[7] dplyr_0.5.0 devtools_1.12.0
[9] gridExtra_2.2.1 gtools_3.5.0
[11] CHBUtils_0.1 genefilter_1.54.2
[13] DESeq2_1.12.4 SummarizedExperiment_1.2.3
[15] Biobase_2.32.0 GenomicRanges_1.24.3
[17] GenomeInfoDb_1.8.7 IRanges_2.6.1
[19] S4Vectors_0.10.3 BiocGenerics_0.18.0
[21] reshape_0.8.5 ggplot2_2.1.0
[23] knitr_1.14
loaded via a namespace (and not attached):
[1] tidyr_0.6.0 splines_3.3.1 Formula_1.2-1
[4] assertthat_0.1 affy_1.50.0 highr_0.6
[7] latticeExtra_0.6-28 yaml_2.1.13 RSQLite_1.0.0
[10] lattice_0.20-34 chron_2.3-47 digest_0.6.10
[13] RColorBrewer_1.1-2 XVector_0.12.1 colorspace_1.2-7
[16] preprocessCore_1.34.0 htmltools_0.3.5 Matrix_1.2-7.1
[19] plyr_1.8.4 XML_3.98-1.4 zlibbioc_1.18.0
[22] xtable_1.8-2 scales_0.4.0 gdata_2.17.0
[25] affyio_1.42.0 BiocParallel_1.6.6 tibble_1.2
[28] annotate_1.50.1 withr_1.0.2 nnet_7.3-12
[31] lazyeval_0.2.0 survival_2.39-5 magrittr_1.5
[34] memoise_1.0.0 evaluate_0.10 GGally_1.2.0
[37] gplots_3.0.1 foreign_0.8-67 BiocInstaller_1.22.3
[40] tools_3.3.1 data.table_1.9.6 formatR_1.4
[43] stringr_1.1.0 munsell_0.4.3 locfit_1.5-9.1
[46] cluster_2.0.5 AnnotationDbi_1.34.4 caTools_1.17.1
[49] grid_3.3.1 RCurl_1.95-4.8 labeling_0.3
[52] bitops_1.0-6 rmarkdown_1.1 gtable_0.2.0
[55] codetools_0.2-15 DBI_0.5-1 R6_2.2.0
[58] Hmisc_3.17-4 KernSmooth_2.23-15 readr_1.0.0
[61] stringi_1.1.2 Rcpp_0.12.7 geneplotter_1.50.0
[64] rpart_4.1-10 acepack_1.3-3.3